Resource allocation plan in a network

ABSTRACT

A method and Resource Allocation Manager Entity for obtaining an improved resource allocation plan for the network. Traffic requests currently exist in a network, each of which having a source, a destination and at least one Quality of Service (QoS) requirement being represented by a QoS value. For each traffic request, at least one potential path consisting of a plurality of links is computed. An iteration matrix is generated having the potential path on first axis, the links on second axis and the QoS requirement on third axis. The iteration matrix is filled by, for each of potential path, distributing the QoS value of the QoS requirement over the links for enabling a gradient space calculation method on the iteration matrix. The gradient space calculation method is applied to the iteration matrix until an iteration marker thereof indicates that the iteration matrix contains the improved resource allocation plan.

TECHNICAL FIELD

The present invention relates to network traffic engineering and, morespecifically, to finding one good network resource allocation assignmentbased on a mathematically resolvable multi-factor system.

BACKGROUND

Traffic engineering mainly refers to attempts made at improvingtraditional best effort routing to get better performance from thenetwork while optimizing its resource allocation. Traffic engineeringmay also take into account Quality of Service (QoS) requirements.

On a different front, work has been initiated to develop the necessaryframework for what is sometimes referred to as Next Generation Networks(NGN). NGN provides separation of transport functions, services andapplications as well as support for several access technologies withdifferent types of services. NGN is also designed to support end to endQoS constraints. NGN aims at using packet switched technology (whereascircuit switching is still commonly used at that level).

What NGN imposes in terms of traffic engineering is to accommodate muchmore diverse needs and characteristics. The existing multi-constraintsrouting mechanism, likewise, are not able to support the expectedrequirements while keeping a manageable level of complexity.Furthermore, the current traffic engineering solutions are focused onadmission control and initial reservation setup, which leads to longterm sub-optimization of network utilization.

The present invention targets the needs for a flexible yet manageableresource allocation mechanism that takes into account longer termnetwork resource allocation.

SUMMARY

A first aspect of the present inventions is directed to a method forobtaining an improved resource allocation plan in a network. A pluralityof traffic requests currently exists in the network, each of whichhaving a source and a destination in the network. Each traffic requestis also associated to at least one Quality of Service (QoS) requirementeach represented by a QoS value. The method comprises a step ofcomputing, for each of the plurality of traffic requests, at least onepotential path consisting of a plurality of links between the source andthe destination thereof. The method than continues with a step ofgenerating an iteration matrix. The iteration matrix has each of the atleast one potential path on a first axis, each of the plurality of linkson a second axis and each of the at least one QoS requirement on a thirdaxis. The method follows with a step of filling the iteration matrix by,for each of the at least one potential path, distributing each of theQoS value of the at least one QoS requirement over the plurality oflinks for enabling a gradient space calculation method on the iterationmatrix. The gradient space calculation method is applied to theiteration matrix until an iteration marker of the gradient spacecalculation method indicates that the iteration matrix contains theimproved resource allocation plan for the network.

A second aspect of the present invention is directed to a ResourceAllocation Manager Entity implemented on a hardware platform. Aplurality of traffic requests currently exists in a network, each ofwhich having a source and a destination in the network and beingassociated to at least one Quality of Service (QoS) requirement. EachQoS requirement is represented by a QoS value. The Resource AllocationManager Entity comprises a Computation Module. The Computation Modulecomputes, for each of the plurality of traffic requests, at least onepotential path consisting of a plurality of links between the source andthe destination thereof. The Computation Module also generates aniteration matrix having each of the at least one potential path on afirst axis, each of the plurality of links on a second axis and each ofthe at least one QoS requirement on a third axis. The Computation Modulefurther fills the iteration matrix by, for each of the at least onepotential path, distributing each of the QoS value of the at least oneQoS requirement over the plurality of links for enabling a gradientspace calculation method on the iteration matrix. The gradient spacecalculation method is applied to the iteration matrix until an iterationmarker of the gradient space calculation method indicates that theiteration matrix contains an improved resource allocation plan for thenetwork.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention may be gained byreference to the following ‘Detailed description’ when taken inconjunction with the accompanying drawings wherein:

FIG. 1 is a topological view of an exemplary network in accordance withthe teachings of the invention;

FIG. 2 shows a flow chart of an exemplary algorithm executed by aresource allocation manager in accordance with the teachings of theinvention;

FIGS. 3A and 3B together referred to as FIG. 3 shows an exemplarymodular representation of a resource allocation manager entity inaccordance with the teachings of the invention; and

FIG. 4 shows an exemplary function between required bandwidth for 20ON-OFF voice connections and packet loss probability in accordance withthe teachings of the invention.

DETAILED DESCRIPTION

The present invention provides a solution to traffic engineering thattakes into account longer term network resource allocation, i.e., thatreconsiders traffic requests currently being handled with the purpose ofimproving network's utilization rather than strictly admitting newtraffic requests based on current network utilization withoutreconsidering current assignments. A resource allocation manager entity(e.g., a resource admission control system) uses predictability oftraffic aggregation (linear function or non-linear convex function) toassign paths respecting Quality of Service (QoS) requirements in anetwork that comprises a plurality of routers (at least two edge routersconnected via at least one intermediate router). A topology of thenetwork needs to be known to the resource allocation manager. Likewise,a traffic matrix that comprises currently handled traffic requests needto be available to the resource allocation manager. In order to obtain asolution to traffic assignment, in the best mode of the invention, atleast a portion of the traffic can take advantage of statisticalmultiplexing, which can be described by a non-linear convex function.Still in the best mode of the invention, the portion of the traffic thatcan be affected using statistical multiplexing is assigned to a singleclass (mono-traffic class). The mono-traffic class can then receiveappropriate treatment in the network taking into account statisticalmultiplexing potential. Furthermore, it should be added that if pluraltraffic types were eventually described under a common statisticalmultiplexing function, the present invention would be able to takeadvantage of such a function in a manner similar to the one describedfor a mono-traffic type.

Reference is now made to the drawings, in which FIG. 1 shows anexemplary topological view of an exemplary network 100 in accordancewith the teachings of the invention. The example of FIG. 1 is chosenwith clarity and simplicity for the purpose of illustrating the presentinvention. Resource allocation in the network 100 is under themanagement of a resource allocation manager entity (RAME) 110. The RAME110 can be implemented using software, hardware or a mix of software andhardware. Both software and hardware could be dedicated to the RAME 110,but are likely to be shared with other capabilities. For instance, thefunctionalities of the RAME 110 are likely to be implemented using aResource Admission Control Subsystem (RACS), but the present inventionis not limited to such an implementation. The network 100 comprises twoedge nodes 120 and 130 and a core border node 140. The nodes 120, 130and 140 represent the entry and/or exit points of the network 100. Thenaming of the nodes 120-140 reflects common naming used in variousstandards. However, the present invention shall not be construed asbeing limited thereto.

Some assumptions are made concerning the network 100 in order for thepresent invention to provide an improved, and useful, resourceallocation plan. A first assumption is that there exists a mechanism fortopology acquisition (or discovery) in the network 100. The presentinvention needs to gain knowledge of the network's 100 topology in areasonably efficient manner, but does not have any requirements as tohow such topology should be acquired. An example of acceptable topologydiscovery can be seen in “Topology discovery in heterogeneous networks”U.S. application Ser. No. 11/933,692 from Yves Lemieux and Paul VitalMahop. Furthermore, it would also be possible to simulate node removal,node addition or workload sharing in the topology of the network tobetter appreciate the potential effect. In a similar manner, trafficrequests currently fulfilled need to be known (e.g., active pathassignments, traffic matrix, etc.). The present invention may or notalso consider traffic requests currently pending admission. Knowledge ofsuch pending request is therefore desirable in the best mode of theinvention yet it is not a prerequisite. Likewise, the present inventionmay also further consider simulated traffic requests or cancellation ofexisting traffic requests e.g., based on load capacity expectation,based on historical or predictable traffic peaks—daily, weekly(weekends), monthly (1^(st) of the month), yearly (e.g., Christmas,Mother's day, etc.) or from punctual event (sport (e.g., Olympic games),religious, political, etc.), etc.

The present invention aims at providing an improved resource allocationplan for the network 100. The improved resource allocation plan, initself, is a tangible result that provides, for instance, administratorsof the network 100 with a view of the capabilities thereof (based onsimulated addition or cancellation of traffic requests and/or simulatedtopology modification). The improved resource allocation plan itself canalso be compared to a current resource allocation situation in order,for instance, to take a decision on whether the improved one should bepropagated in the network 100 (e.g., based on the extent of theimprovement). The improved resource allocation plan can be used, workedon and/or stored on any kind of digital media (computer RAM or ROM, diskRAM or ROM, USB key, etc.). While the propagation of the improvedresource allocation plan in the network 100 is not core to theinvention, the potential of the invention is better observed bypropagating the improved resource allocation plan therein and,therefore, by using an efficient propagation mechanism in the network100. For instance, Resource Reservation Protocol (RSVP) or similar couldbe used for propagating the improved resource allocation plan.

FIG. 1 further shows intermediates routers R1 150, R2 160, R3 170 and R4180 in the network 100. The intermediate routers 160-180 are connectedvia links 1-8 thereby enabling traffic to be exchanged between the twoedge nodes 120 and 130 and the border core node 140.

In the context of the example of FIG. 1 and for illustrating theinvention, an example with two traffic requests will be taken. A firstrequest is for transiting 1000 voice-only traffic connections from edgenode 1 120 to core border node 140. The first request has a first QoSrequirement of maximum delay of 21 ms and a second QoS requirement ofminimum bandwidth of 100 Mbps. A second request is for transiting 500voice-only traffic connections from edge node 2 130 to core border node140. The second request has a first QoS requirement of maximum delay of21 ms and a second QoS requirement of minimum bandwidth of 60 Mbps. Itis assumed that the transit on each link brings about a same delay of 5ms. This assumption is deemed reasonable in the context of a corenetwork, which makes it easy to determine that only the paths betweenthe entry and exit nodes 120-140 having a maximum of 4 links (3traversed routers) are to be considered. It should be noted thatdescribing the delay in terms of links used instead of number oftraversed routers is chosen for simplicity, but does not affect theteachings of the invention. One way or the other provides a way oflimiting the length of the path to be used based on maximum QoS delay(in terms of hops or links). It should further be noted that any mannerby which delay requirement is considered would be good in the context ofthe present invention (e.g., delay measurement, information contained intopology, etc.). Of course, delay may not be a requirement at all incertain cases.

Another assumption made in the context of the example of FIG. 1 is thatthere exists a function that provides, for a required bandwidth, themaximum tolerated packet loss (%). Such a function is used to provide animproved resource allocation plan based on packet loss rather thandirectly on bandwidth required. FIG. 4 is a graph 400 showing anexemplary function 410 between required bandwidth for 20 ON-OFF voiceconnections and packet loss probability in accordance with the teachingsof the invention.

The graph 400 shows an example of the total required bandwidth for 20ON-OFF voice connections with respect to the packet loss probability.The function 410 showed further takes into account a statisticalmultiplexing function that is applicable to voice traffic connections.It is however important to note that the example taken in the context ofFIG. 1 to illustrate the present invention does not depend on the natureof the function 410. It assumes that a function exist between thebandwidth and the packet loss (that may involve statistical multiplexingor not) and that they are continuous and convex (including simplylinear, which is the simplest continuous and convex function).Notwithstanding the exemplary graph 400 of FIG. 4, and for the purposeof the present example, a bandwidth of 100 Mbps is assumed to betranslated into a maximum packet loss of 12‰ and a bandwidth of 60 Mbpsis assumed to be translated into a maximum packet loss of 24‰.

An iteration matrix (follows) is built based on the foregoing. It liststhe potential paths for each request on the first axis and the linkspotentially used by such paths on the second axis. Since only a singleQoS requirement of bandwidth translated in packet loss probability isused, a third axis showing the different QoS requirements is notnecessary, but could be used in certain other applications. Theiteration matrix will be fed to a gradient space calculation method thatwill iteratively get closer to an improved resource allocation plan(contained in the iteration matrix itself). It should be noted that theiteration matrix needs to contain, from the starting point, a solutionthat is mathematically valid in order for the gradient space calculationmethod to converge to a valid solution. The values that are inputted inthe iteration matrix to start processing represent the packet loss ‰. Asimple way of respecting the need for a mathematically valid initialproposition is to equally distribute the requirement over the number oflinks to be used as shown in the first matrix below (again, changing thelinks used for intermediate routers traversed does not affect the logicof the invention).

1 2 3 4 5 6 7 8 120-140a 4 4 4 120-140b 3 3 3 3 120-140c 3 3 3 3130-140a 8 8 8 130-140b 6 6 6 6 130-140c 6 6 6 6 Initial situation:delay max of 21 ms for both requests delay per hop of 5 ms voice trafficin both requests 100 Mbps for 120-140; 60 Mbps for 130-140

The matrix above is thus fed in to the gradient space calculationmethod, which provides, for example after 100 iterations, the followingiteration matrix from which improper solutions are marked as such (orremoved). There exists a marker of iteration completion in the gradientspace calculation method as Lagrange multipliers (which are part of thelogic behind the gradient space calculation method). It is known thatthe gradient space calculation method stops converging (i.e. no moreimprovement foreseen from one iteration to the next) when all theLagrange multipliers are greater than 0.

As all the Lagrange multipliers are not greater than 0 after 100iterations, the gradient space calculation method continues and providesthe following iteration matrix after 500 iterations.

Since all the Lagrange multipliers are now greater than 0, the iterationmatrix comprises a solution to the proposed problem cannot be improvedfrom one iteration to the next. The result contained in the iterationmatrix, in the context of the present example, is considered as theimproved resource allocation plan. It is then possible to translate theresult into bandwidth requirement per link based on the packet lossprobability contained in the iteration matrix using the function usedpreviously. Furthermore, the bandwidth requirement can be translatedinto class assignment. The class assignments can then, if needed, besent in the network 100 to implement the improved resource allocationplan.

FIG. 2 shows a flow chart of an exemplary algorithm executed in thenetwork 100, for instance, by a resource allocation manager inaccordance with the teachings of the present invention.

As mentioned previously, an assumption is made that topology and currentallocation be known. This is shown on FIG. 2 as contained in a TrafficMatrix & Physical Topology 2010. The Traffic Matrix & Physical Topology2010 comprises the traffic requests being handled. The traffic requestswould be aggregated based on their source and destination in the network100, but also, in an exemplary implementation corresponding to the bestmode known to the inventors, on their respective traffic types to formtraffic trunks. Aggregating on traffic types enables statisticalmultiplexing functions to be applied to payload of certain traffic typesto further enhance resource utilization. The statistical multiplexingfunction is evaluated over time to allow for a prediction of trunkutilization improvement as a function of the number of flows aggregated.So, to a number of aggregated flows corresponds a trunk utilizationimprovement in % (or in corresponding decimal fraction), which ispotentially used in the present invention. It is also desirable toaggregate on QoS requirements that are common. Alternatively, it wouldbe possible to use the most aggressive QoS requirements of a giventraffic trunk for the purpose of the present invention.

The example of FIG. 2 starts with a step of admitting new traffic basedon short term criteria 2100. The admission 2100 is done using theTraffic Matrix & Physical Topology 2010, among other things (e.g.,credentials, etc.). However, the step 2100 alone does not considermodifications to current resource allocations handled in the network100.

Then, a trigger reassignment event is detected 2110. The trigger event2110 causes a reevaluation of the current traffic allocations (andpotentially new traffic requests pending). The trigger event 2110 can beof various nature such as expiration of a timer (run reassignment every2 hours), incapacity to admit a new traffic request in the network 100,change of QoS requirement for one traffic request, degradation ofperceived QoS in the network 100, loss of equipment in the network 100,etc. As such, the trigger event 2110 falls outside the scope of thepresent invention.

Following the trigger detection 2110, each traffic trunk is treated(2120) to compute at least one feasible path for the currently treatedtraffic trunk (2130). The feasible path meets the source and destinationrequirement of the traffic trunk being treated. However, the feasiblepath is kept as a potential path for further analysis, in the example ofFIG. 2, only if it meets the delay requirement associated thereto(2140). A simple manner of evaluating the delay of a path is to countthe number of hops and use a same value of delay per hop. Such anassumption is usually made in the context of core networks. Of course,many different ways of evaluating delay requirement fulfillment can beused in the context of the present invention.

The steps 2120-2140 are repeated until there is no more traffic trunk totreat (2150). It should be noted that, in some implementations, only asubset of the traffic trunks or traffic requests handled in the network100 could be submitted to the present algorithm. In such situations, theimportant aspect is that remaining capacity available to the trafficrequests/trunks treated by the present algorithm be known in thetopology 2010.

Once all potential paths are identified, the algorithm follows withgeneration of an iteration matrix 2155 to be used by a gradient spacecalculation method in order to obtain an improved resource allocationplan in the work 100. The iteration matrix has the potential paths on afirst axis, each of the plurality of links of each potential path on asecond axis and each QoS requirement on a third axis. In the example ofFIG. 2, we consider a bandwidth requirement at this point that istranslated in step 2155, and as explained earlier, to packet lossprobability.

The iteration matrix is then filled by, for each potential path,distributing each of the packet loss probability over the plurality oflinks (2160). The step of distributing, in the case of packet loss,corresponds to partitioning, which needs to enable the gradient spacecalculation method on the iteration matrix (i.e., a mathematicallyacceptable solution needs to be entered in the iteration matrix). Afunction could be used to generate the step of distributing (a lessefficient algorithm, a first run (complete or partial) of the presentalgorithm, etc.). The gradient space calculation method is then appliedto the iteration matrix (as explicated below in steps 2170-2240). Whilethe present invention uses the gradient space calculation method withoutmodifying its behavior, some information on the data treatment itself ishereby provided below. It should however be noted that, in order toobtain the result of an improved resource allocation plan for thenetwork 100, knowing that the gradient space calculation method providesa solution and applying as prescribed herein is sufficient.

The gradient space calculation method starts by computing a subspacethat is composed, in the present example, of active QoS constraints2170. It then builds a projection matrix reflecting the tangent subspace2180. The gradient space calculation method further computes theprojection of the gradient on the iteration matrix 2190. At this stage,the gradient space calculation method should have taken advantage of aneventual statistical multiplexing advantage provided by the mono-traffictype traffic trunk. The statistical multiplexing advantage is likely tobe described by a function, but could also be described by a table ofvalues with equal advantages to the present invention. If a feasiblesearch direction exists in the gradient space (2200), the configurationthat minimizes the objective, over the gradient direction is computed2210. Otherwise, the gradient space calculation method computes Lagrangemultiplier for every QoS constraint 2220. The Lagrange multipliers arean iteration marker of the iteration matrix. If all the Lagrangemultipliers are greater than 0 (2230), it indicates that the iterationmatrix contains the improved resource allocation plan for the network ofthe gradient space calculation method. If at least one of the Lagrangemultipliers is below 0, then further improvement can be made and thegradient space calculation method follows with removal, from the set ofconstraints, the constraint having the smallest multiplier 2240.

Once the iteration matrix contains the improved resource allocation planfor the network from the gradient space calculation method, the contentthereof may be used for all good reasons previously detailed. One suchuse is for path assignment, in which case the content of the iterationmatrix (a.k.a. the result) is translated into path assignments 2250,which can be communicated to affected routers 2260 in the network 100.The decision to communicate (or propagate) path assignments iscontextual (as explained hereinabove). If ever sent, the affectedrouters may further take a decision locally concerning application ofthe path assignments (e.g., policy-based decision based on identity ofsender).

FIGS. 3A and 3B together referred to as FIG. 3 shows an exemplarymodular representation of a Resource Allocation Manager Entity (RAME)300 in accordance with the teachings of the invention. The RAME 300 maybe implemented on a hardware platform in the network 100 or in anotherlocation from which the network 100 can be managed. A plurality oftraffic requests currently exists in the network and each of theplurality of traffic requests has a source and a destination in thenetwork. Each traffic request is also associated to at least one Qualityof Service (QoS) requirement each represented by a QoS value. The RAME300 comprises a Computation Module 320 that computes, for each of theplurality of traffic requests, at least one potential path consisting ofa plurality of links between the source and the destination thereof. TheComputation Module 320 generates an iteration matrix having each of theat least one potential path on a first axis, each of the plurality oflinks on a second axis and each of the at least one QoS requirement on athird axis. The Computation Module 320 also fills the iteration matrixby, for each of the at least one potential path, distributing each ofthe QoS value of the at least one QoS requirement over the plurality oflinks for enabling a gradient space calculation method on the iterationmatrix. The gradient space calculation method is applied to theiteration matrix until an iteration marker of the gradient spacecalculation method indicates that the iteration matrix contains animproved resource allocation plan for the network.

The at least one QoS requirement may be a delay requirement for whichthe QoS value is a maximum delay value. Each of the plurality of linksmay be presumed to bring upon a same delay. In such a case, theComputation Module 320 further computes the potential path by computing,for each of the plurality of traffic requests, at least one potentialpath between the source and the destination, wherein each of the atleast one potential path consists of a limited number of linkscorresponding to the maximum delay value.

The Computation Module 320 of the RAME 300 may further fill theiteration matrix by further dividing each of the QoS value equally overthe plurality of links. If the at least one QoS requirement comprises abandwidth requirement for which the QoS value is a minimum bandwidthvalue and if a function exists to represent the bandwidth requirement interms of a packet loss probability, then the Computation Module 320 mayfurther fill the iteration matrix by further performing the distributionthrough partitioning the packet loss probability equally over theplurality of links

At least one or more of the traffic requests may further be associatedto a single traffic type for which a statistical multiplexingenhancement function exists. Then, the gradient space calculation methodmay be performed taking into account the statistical multiplexingenhancement function.

The RAME 300 may further comprise an Enforcement Module 330 that,following indication that the iteration matrix contains the improvedresource allocation plan for the network, translates the iterationmatrix into a plurality of path assignments. Each path assignment couldcomprise a plurality of class assignments for which a function exists torepresent each QoS value of the at least one QoS requirement in terms ofclass assignments. Then, the RAME 300 may also further comprise aCommunication Module 340 that communicates at least a portion of thepaths assignments in the network.

The Computation Module 320 may further compute potential path onlyfollowing a trigger event in the network (e.g., an incapacity of thenetwork 100 to accommodate a new traffic request therein and anexpiration of a timer in the network 100). The RAME 300 may also furthercomprise a Monitoring Module 310 that receives an allocation planrevision request.

Reference is now made concurrently to FIG. 1 and FIG. 3B, which showsthe RAME 300 in the context of a Resource Admission Control Sub-system(RACS) 300′. The RACS 300′ is likely used, among others, in the contextof Telecommunication and Internet converged Services and Protocols forAdvanced Networking (TISPAN). References to the network 100 and nodes120-150 presented on FIG. 1 are repeated in the context of TISPAN inFIG. 3B into network 100′ and nodes 120′-150′. Interactions between theRACS 300′ and an Access node 120′ (similar to edge routers 120 or 130)are made via a Ra interface of TISPAN. Interactions between the RACS300′ and the intermediate router 150′ (similar to intermediate routers150-180) are made via a Re interface of TISPAN. Interactions between theRACS 300′ and the Core border node 140′ (similar to Core border router140) are made via a Ia interface of TISPAN or via a Reference Pointinterface in the more specific context 3rd Generation PartnershipProject (3GPP).

FIG. 3B further shows a Customer Premise Equipment (CPE) 500 that is therequesting entity when it comes to traffic transition in the network100′ and an Application Function (AF) 400 connected to the RACS 300′through a Gq′ interface. The AF 400 is likely to be requesting revisionof resource allocation plan or another trigger reassignment event, forinstance, in the context of the present invention.

Although several examples of the present invention have been illustratedin the accompanying drawings and described in the foregoing description,it will be understood that the invention is not limited to theembodiments disclosed, but is capable of numerous rearrangements,modifications and substitutions without departing from the teachings ofthe present invention. In general, statements made in the description ofthe present invention do not necessarily limit any of the variousclaimed aspects of the present invention. Moreover, some statements mayapply to some inventive features but not to others. In the drawings,like or similar elements are designated with identical referencenumerals throughout the several views, and the various elements depictedare not necessarily drawn to scale.

1. A method for obtaining an improved resource allocation plan in anetwork, wherein a plurality of traffic requests currently exists in thenetwork, each of the plurality of traffic requests having a source and adestination in the network and being associated to at least one Qualityof Service (QoS) requirement each represented by a QoS value, the methodcomprising the steps of: computing, using a hardware platform, for eachof the plurality of traffic requests, at least one potential pathconsisting of a plurality of links between the source and thedestination thereof; generating an iteration matrix having each of theat least one potential path on a first axis, each of the plurality oflinks on a second axis and each of the at least one QoS requirement on athird axis; and filling the iteration matrix by, for each of the atleast one potential path, distributing each of the QoS value of the atleast one QoS requirement over the plurality of links for enabling agradient space calculation method on the iteration matrix, the gradientspace calculation method being applied to the iteration matrix until aniteration marker of the gradient space calculation method indicates thatthe iteration matrix contains the improved resource allocation plan forthe network, wherein the improved resource allocation plan is stored inmemory.
 2. The method of claim 1 wherein the at least one QoSrequirement is a delay requirement for which the QoS value is a maximumdelay value and each of the plurality of links is presumed to bring upona same delay, the step of computing further comprising computing, foreach of the plurality of traffic requests, at least one potential pathbetween the source and the destination, wherein each of the at least onepotential path consists of a limited number of links corresponding tothe maximum delay value.
 3. The method of claim 1 wherein the step offilling the iteration matrix by distributing each of the QoS valuefurther comprises dividing each of the QoS value equally over theplurality of links.
 4. The method of claim 3 wherein the at least oneQoS requirement comprises a bandwidth requirement for which the QoSvalue is a minimum bandwidth value and wherein a function exists torepresent the bandwidth requirement in terms of a packet lossprobability, the step of filling the iteration matrix by distributingeach of the QoS value further comprising partitioning the packet lossprobability equally over the plurality of links.
 5. The method of claim1 wherein at least a first of the plurality of traffic requests isfurther associated to a single traffic type.
 6. The method of claim 5wherein a statistical multiplexing enhancement function exists for thesingle traffic type, the gradient space calculation method beingperformed taking into account the statistical multiplexing enhancementfunction.
 7. The method of claim 1, following indication that theiteration matrix contains the improved resource allocation plan for thenetwork, further comprising a step of translating the iteration matrixinto a plurality of path assignments, each path assignment comprising aplurality of class assignments, wherein a function exists to representeach QoS value of the at least one QoS requirement in terms of classassignments.
 8. The method of claim 7 further comprising a step ofcommunicating at least a portion of the paths assignments in thenetwork.
 9. The method of claim 1 wherein the step of computing isperformed following a trigger event in the network.
 10. The method ofclaim 9 wherein the trigger event in the network comprises one of anincapacity of the network 100 to accommodate a new traffic requesttherein and an expiration of a timer in the network
 100. 11. The methodof claim 1 further comprising a first step receiving an allocation planrevision request prior to the step of computing.
 12. A ResourceAllocation Manager Entity implemented on a hardware platform, wherein aplurality of traffic requests currently exists in a network, each of theplurality of traffic requests having a source and a destination in thenetwork and being associated to at least one Quality of Service (QoS)requirement each represented by a QoS value, the Resource AllocationManager Entity comprising: a Computation Module that: computes, for eachof the plurality of traffic requests, at least one potential pathconsisting of a plurality of links between the source and thedestination thereof, generates an iteration matrix having each of the atleast one potential path on a first axis, each of the plurality of linkson a second axis and each of the at least one QoS requirement on a thirdaxis; and fills the iteration matrix by, for each of the at least onepotential path, distributing each of the QoS value of the at least oneQoS requirement over the plurality of links for enabling a gradientspace calculation method on the iteration matrix, the gradient spacecalculation method being applied to the iteration matrix until aniteration marker of the gradient space calculation method indicates thatthe iteration matrix contains an improved resource allocation plan forthe network.
 13. The Resource Allocation Manager Entity of claim 12wherein the at least one QoS requirement is a delay requirement forwhich the QoS value is a maximum delay value and each of the pluralityof links is presumed to bring upon a same delay, wherein the ComputationModule further computes by computing, for each of the plurality oftraffic requests, at least one potential path between the source and thedestination, wherein each of the at least one potential path consists ofa limited number of links corresponding to the maximum delay value. 14.The Resource Allocation Manager Entity claim 12 wherein the ComputationModule further fills the iteration matrix by further dividing each ofthe QoS value equally over the plurality of links.
 15. The ResourceAllocation Manager Entity of claim 14 wherein the at least one QoSrequirement comprises a bandwidth requirement for which the QoS value isa minimum bandwidth value and wherein a function exists to represent thebandwidth requirement in terms of a packet loss probability, wherein theComputation Module fills the iteration matrix by further distributingthe packet loss probability equally over the plurality of links.
 16. TheResource Allocation Manager Entity of claim 12 wherein at least a firstof the plurality of traffic requests is further associated to a singletraffic type.
 17. The Resource Allocation Manager Entity claim 16wherein a statistical multiplexing enhancement function exists for thesingle traffic type, the gradient space calculation method beingperformed taking into account the statistical multiplexing enhancementfunction.
 18. The Resource Allocation Manager Entity claim 12, followingindication that the iteration matrix contains the improved resourceallocation plan for the network, further comprising an EnforcementModule that translates the iteration matrix into a plurality of pathassignments, each path assignment comprising a plurality of classassignments, wherein a function exists to represent each QoS value ofthe at least one QoS requirement in terms of class assignments.
 19. TheResource Allocation Manager Entity claim 18 further comprising aCommunication Module that communicates at least a portion of the pathsassignments in the network.
 20. The Resource Allocation Manager Entityclaim 12 wherein the Computation Module computes following a triggerevent in the network.
 21. The Resource Allocation Manager Entity claim20 wherein the trigger event in the network comprises one of anincapacity of the network 100 to accommodate a new traffic requesttherein and an expiration of a timer in the network
 100. 22. TheResource Allocation Manager Entity claim 12 further comprising aMonitoring Module that receives an allocation plan revision request.